Methodology and Results of the Study of Seed Potato Tuber Parameters Using Digital Tools

Methodology and Results of the Study of Seed Potato Tuber Parameters Using Digital Tools | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Methodology and Results of the Study of Seed Potato Tuber Parameters Using Digital Tools Zhandos Shynybay, Jakhfer Alikhanov, Maigul Bakytova, Aidar Moldazhanov, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7057294/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The article presents a methodology for the quantitative assessment of seed potato tuber and mini-tuber parameters, comparing traditional manual measurements with an automated digital method using a machine vision system based on OpenCV in Python. Five major Kazakh potato varieties—Astana, Alliance, Janaysan, Narly, and Eden—were analyzed. Each tuber’s mass and linear dimensions (length, width, thickness) were measured manually and digitally. The automated setup uses two cameras to capture images from perpendicular planes, allowing calculation of mass, dimensions, area, and perimeter from images. An algorithm was developed to convert image data from pixels to millimeters and ensure accurate physical measurements. Results showed close agreement between manual and digital methods, with a relative error not exceeding 1.6% for mass and 3.0% for dimensions. Shape descriptors such as index and form coefficient were also calculated. Regression models for mass prediction were developed, offering high accuracy and potential for refinement. The digital method increased measurement productivity by seven times compared to manual approaches. The findings and regression equations will contribute to the development of machine learning algorithms for automated varietal classification of potato tubers based on their physical traits. potato tubers physical assessment machine vision image analysis food quality non-destructive evaluation Full Text Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7057294","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":482704194,"identity":"ba29bbd8-916f-4d3c-9559-85a960fac386","order_by":0,"name":"Zhandos Shynybay","email":"","orcid":"","institution":"Almaty University of Power Engineering and Telecommunication","correspondingAuthor":false,"prefix":"","firstName":"Zhandos","middleName":"","lastName":"Shynybay","suffix":""},{"id":482704195,"identity":"9c23dbd7-49ef-4daf-85b0-9090355d97db","order_by":1,"name":"Jakhfer Alikhanov","email":"","orcid":"","institution":"Kazakh National 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